NYC Traffic Accidents

Motor vehicle collisions reported by the New York City Police Department from January-August 2020. Each record represents an individual collision, including the date, time and location of the accident (borough, zip code, street name, latitude/longitude), vehicles and victims involved, and contributing factors.

Data Source : https://www.mavenanalytics.io/data-playground

----Load data----

import pandas as pd
import numpy as np
from pandas import Timestamp
import datetime as dt
from datetime import datetime

df= pd.read_csv("NYCAccidents2020.csv")
df.head(5)

	CRASH DATE	CRASH TIME	BOROUGH	ZIP CODE	LATITUDE	LONGITUDE	LOCATION	ON STREET NAME	CROSS STREET NAME	OFF STREET NAME	...	CONTRIBUTING FACTOR VEHICLE 2	CONTRIBUTING FACTOR VEHICLE 3	CONTRIBUTING FACTOR VEHICLE 4	CONTRIBUTING FACTOR VEHICLE 5	COLLISION_ID	VEHICLE TYPE CODE 1	VEHICLE TYPE CODE 2	VEHICLE TYPE CODE 3	VEHICLE TYPE CODE 4	VEHICLE TYPE CODE 5
0	2020-08-29	15:40:00	BRONX	10466.0	40.89210	-73.833760	POINT (-73.83376 40.8921)	PRATT AVENUE	STRANG AVENUE	NaN	...	Unspecified	NaN	NaN	NaN	4342908	Sedan	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN
1	2020-08-29	21:00:00	BROOKLYN	11221.0	40.69050	-73.919914	POINT (-73.919914 40.6905)	BUSHWICK AVENUE	PALMETTO STREET	NaN	...	Unspecified	NaN	NaN	NaN	4343555	Sedan	Sedan	NaN	NaN	NaN
2	2020-08-29	18:20:00	NaN	NaN	40.81650	-73.946556	POINT (-73.946556 40.8165)	8 AVENUE	NaN	NaN	...	NaN	NaN	NaN	NaN	4343142	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN	NaN
3	2020-08-29	00:00:00	BRONX	10459.0	40.82472	-73.892960	POINT (-73.89296 40.82472)	NaN	NaN	1047 SIMPSON STREET	...	Unspecified	Unspecified	Unspecified	NaN	4343588	Station Wagon/Sport Utility Vehicle	Station Wagon/Sport Utility Vehicle	Sedan	Motorcycle	NaN
4	2020-08-29	17:10:00	BROOKLYN	11203.0	40.64989	-73.933890	POINT (-73.93389 40.64989)	NaN	NaN	4609 SNYDER AVENUE	...	Unspecified	NaN	NaN	NaN	4342953	Sedan	Sedan	NaN	NaN	NaN

5 rows × 29 columns

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 74881 entries, 0 to 74880
Data columns (total 29 columns):
 #   Column                         Non-Null Count  Dtype  
---  ------                         --------------  -----  
 0   CRASH DATE                     74881 non-null  object 
 1   CRASH TIME                     74881 non-null  object 
 2   BOROUGH                        49140 non-null  object 
 3   ZIP CODE                       49134 non-null  float64
 4   LATITUDE                       68935 non-null  float64
 5   LONGITUDE                      68935 non-null  float64
 6   LOCATION                       68935 non-null  object 
 7   ON STREET NAME                 55444 non-null  object 
 8   CROSS STREET NAME              35681 non-null  object 
 9   OFF STREET NAME                19437 non-null  object 
 10  NUMBER OF PERSONS INJURED      74881 non-null  int64  
 11  NUMBER OF PERSONS KILLED       74881 non-null  int64  
 12  NUMBER OF PEDESTRIANS INJURED  74881 non-null  int64  
 13  NUMBER OF PEDESTRIANS KILLED   74881 non-null  int64  
 14  NUMBER OF CYCLIST INJURED      74881 non-null  int64  
 15  NUMBER OF CYCLIST KILLED       74881 non-null  int64  
 16  NUMBER OF MOTORIST INJURED     74881 non-null  int64  
 17  NUMBER OF MOTORIST KILLED      74881 non-null  int64  
 18  CONTRIBUTING FACTOR VEHICLE 1  74577 non-null  object 
 19  CONTRIBUTING FACTOR VEHICLE 2  59285 non-null  object 
 20  CONTRIBUTING FACTOR VEHICLE 3  6765 non-null   object 
 21  CONTRIBUTING FACTOR VEHICLE 4  1851 non-null   object 
 22  CONTRIBUTING FACTOR VEHICLE 5  523 non-null    object 
 23  COLLISION_ID                   74881 non-null  int64  
 24  VEHICLE TYPE CODE 1            74246 non-null  object 
 25  VEHICLE TYPE CODE 2            53638 non-null  object 
 26  VEHICLE TYPE CODE 3            6424 non-null   object 
 27  VEHICLE TYPE CODE 4            1771 non-null   object 
 28  VEHICLE TYPE CODE 5            503 non-null    object 
dtypes: float64(3), int64(9), object(17)
memory usage: 16.6+ MB

---- Start the data cleaning----

We need to make sure the data is clean before starting your analysis. As a reminder, we should check for:

• Duplicate records
• Consistent formatting
• Missing values
• Obviously wrong values (x)

Duplicate Records

How many duplicate transaction records are there?

dup_rows = df.duplicated().sum()
dup_rows

0

Drop Duplicate Records

Drop the duplicated records.

# your code here
df = df.drop_duplicates()
df.head()

	CRASH DATE	CRASH TIME	BOROUGH	ZIP CODE	LATITUDE	LONGITUDE	LOCATION	ON STREET NAME	CROSS STREET NAME	OFF STREET NAME	...	CONTRIBUTING FACTOR VEHICLE 2	CONTRIBUTING FACTOR VEHICLE 3	CONTRIBUTING FACTOR VEHICLE 4	CONTRIBUTING FACTOR VEHICLE 5	COLLISION_ID	VEHICLE TYPE CODE 1	VEHICLE TYPE CODE 2	VEHICLE TYPE CODE 3	VEHICLE TYPE CODE 4	VEHICLE TYPE CODE 5
0	2020-08-29	15:40:00	BRONX	10466.0	40.89210	-73.833760	POINT (-73.83376 40.8921)	PRATT AVENUE	STRANG AVENUE	NaN	...	Unspecified	NaN	NaN	NaN	4342908	Sedan	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN
1	2020-08-29	21:00:00	BROOKLYN	11221.0	40.69050	-73.919914	POINT (-73.919914 40.6905)	BUSHWICK AVENUE	PALMETTO STREET	NaN	...	Unspecified	NaN	NaN	NaN	4343555	Sedan	Sedan	NaN	NaN	NaN
2	2020-08-29	18:20:00	NaN	NaN	40.81650	-73.946556	POINT (-73.946556 40.8165)	8 AVENUE	NaN	NaN	...	NaN	NaN	NaN	NaN	4343142	Station Wagon/Sport Utility Vehicle	NaN	NaN	NaN	NaN
3	2020-08-29	00:00:00	BRONX	10459.0	40.82472	-73.892960	POINT (-73.89296 40.82472)	NaN	NaN	1047 SIMPSON STREET	...	Unspecified	Unspecified	Unspecified	NaN	4343588	Station Wagon/Sport Utility Vehicle	Station Wagon/Sport Utility Vehicle	Sedan	Motorcycle	NaN
4	2020-08-29	17:10:00	BROOKLYN	11203.0	40.64989	-73.933890	POINT (-73.93389 40.64989)	NaN	NaN	4609 SNYDER AVENUE	...	Unspecified	NaN	NaN	NaN	4342953	Sedan	Sedan	NaN	NaN	NaN

5 rows × 29 columns

Consistent formatting

#Convert object to datatime
df['CRASH DATE'] = pd.to_datetime(df['CRASH DATE'])
df['CRASH TIME'] = pd.to_datetime(df['CRASH TIME'],format='%H:%M:%S').dt.time

Missing Values

How many missing values are there?

df.isnull().sum() #Find the number of null per each columns

CRASH DATE                           0
CRASH TIME                           0
BOROUGH                          25741
ZIP CODE                         25747
LATITUDE                          5946
LONGITUDE                         5946
LOCATION                          5946
ON STREET NAME                   19437
CROSS STREET NAME                39200
OFF STREET NAME                  55444
NUMBER OF PERSONS INJURED            0
NUMBER OF PERSONS KILLED             0
NUMBER OF PEDESTRIANS INJURED        0
NUMBER OF PEDESTRIANS KILLED         0
NUMBER OF CYCLIST INJURED            0
NUMBER OF CYCLIST KILLED             0
NUMBER OF MOTORIST INJURED           0
NUMBER OF MOTORIST KILLED            0
CONTRIBUTING FACTOR VEHICLE 1      304
CONTRIBUTING FACTOR VEHICLE 2    15596
CONTRIBUTING FACTOR VEHICLE 3    68116
CONTRIBUTING FACTOR VEHICLE 4    73030
CONTRIBUTING FACTOR VEHICLE 5    74358
COLLISION_ID                         0
VEHICLE TYPE CODE 1                635
VEHICLE TYPE CODE 2              21243
VEHICLE TYPE CODE 3              68457
VEHICLE TYPE CODE 4              73110
VEHICLE TYPE CODE 5              74378
dtype: int64

#Drop the missing values in 'LOCATION' column
df = df[df['LOCATION'].notna()]
df = df[df['CONTRIBUTING FACTOR VEHICLE 1'].notna()]

Rename column name

df.rename(columns = {"CRASH DATE": "crash_date"}, inplace=True)
df.rename(columns = {"CRASH TIME": "crash_time"}, inplace=True)
df.rename(columns = {"BOROUGH": "borough"}, inplace=True)
df.rename(columns = {"ZIP CODE": "zip_code"}, inplace=True)
df.rename(columns = {"LATITUDE": "latitude"}, inplace=True)
df.rename(columns = {"LONGITUDE": "longitude"}, inplace=True)
df.rename(columns = {"LOCATION": "location"}, inplace=True)
df.rename(columns = {"ON STREET NAME": "on_street_name"}, inplace=True)
df.rename(columns = {"CROSS STREET NAME": "cross_street_name"}, inplace=True)
df.rename(columns = {"OFF STREET NAME": "off_street_name"}, inplace=True)
df.rename(columns = {"NUMBER OF PERSONS INJURED": "number_of_persons_injured"}, inplace=True)
df.rename(columns = {"NUMBER OF PERSONS KILLED": "number_of_persons_killed"}, inplace=True)
df.rename(columns = {"NUMBER OF PEDESTRIANS INJURED": "number_of_pedestrians_injured"}, inplace=True)
df.rename(columns = {"NUMBER OF PEDESTRIANS KILLED": "number_of_pedestrians_killed"}, inplace=True)
df.rename(columns = {"NUMBER OF CYCLIST INJURED": "number_of_cyclist_injured"}, inplace=True)
df.rename(columns = {"NUMBER OF CYCLIST KILLED": "number_of_cyclist_killed"}, inplace=True)
df.rename(columns = {"NUMBER OF MOTORIST INJURED": "number_of_motorist_injured"}, inplace=True)
df.rename(columns = {"NUMBER OF MOTORIST KILLED": "number_of_motorist_killed"}, inplace=True)
df.rename(columns = {"CONTRIBUTING FACTOR VEHICLE 1": "contributing_factor_vehicle_1"}, inplace=True)
df.rename(columns = {"CONTRIBUTING FACTOR VEHICLE 2": "contributing_factor_vehicle_2"}, inplace=True)
df.rename(columns = {"CONTRIBUTING FACTOR VEHICLE 3": "contributing_factor_vehicle_3"}, inplace=True)
df.rename(columns = {"CONTRIBUTING FACTOR VEHICLE 4": "contributing_factor_vehicle_4"}, inplace=True)
df.rename(columns = {"CONTRIBUTING FACTOR VEHICLE 5": "contributing_factor_vehicle_5"}, inplace=True)
df.rename(columns = {"COLLISION_ID": "collision_id"}, inplace=True)
df.rename(columns = {"VEHICLE TYPE CODE 1": "vehicle_type_code_1"}, inplace=True)
df.rename(columns = {"VEHICLE TYPE CODE 2": "vehicle_type_code_2"}, inplace=True)
df.rename(columns = {"VEHICLE TYPE CODE 3": "vehicle_type_code_3"}, inplace=True)
df.rename(columns = {"VEHICLE TYPE CODE 4": "vehicle_type_code_4"}, inplace=True)
df.rename(columns = {"VEHICLE TYPE CODE 5": "vehicle_type_code_5"}, inplace=True)

df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 68647 entries, 0 to 74880
Data columns (total 29 columns):
 #   Column                         Non-Null Count  Dtype         
---  ------                         --------------  -----         
 0   crash_date                     68647 non-null  datetime64[ns]
 1   crash_time                     68647 non-null  object        
 2   borough                        47517 non-null  object        
 3   zip_code                       47511 non-null  float64       
 4   latitude                       68647 non-null  float64       
 5   longitude                      68647 non-null  float64       
 6   location                       68647 non-null  object        
 7   on_street_name                 49872 non-null  object        
 8   cross_street_name              32734 non-null  object        
 9   off_street_name                18775 non-null  object        
 10  number_of_persons_injured      68647 non-null  int64         
 11  number_of_persons_killed       68647 non-null  int64         
 12  number_of_pedestrians_injured  68647 non-null  int64         
 13  number_of_pedestrians_killed   68647 non-null  int64         
 14  number_of_cyclist_injured      68647 non-null  int64         
 15  number_of_cyclist_killed       68647 non-null  int64         
 16  number_of_motorist_injured     68647 non-null  int64         
 17  number_of_motorist_killed      68647 non-null  int64         
 18  contributing_factor_vehicle_1  68647 non-null  object        
 19  contributing_factor_vehicle_2  54520 non-null  object        
 20  contributing_factor_vehicle_3  6200 non-null   object        
 21  contributing_factor_vehicle_4  1704 non-null   object        
 22  contributing_factor_vehicle_5  489 non-null    object        
 23  collision_id                   68647 non-null  int64         
 24  vehicle_type_code_1            68274 non-null  object        
 25  vehicle_type_code_2            49155 non-null  object        
 26  vehicle_type_code_3            5885 non-null   object        
 27  vehicle_type_code_4            1629 non-null   object        
 28  vehicle_type_code_5            471 non-null    object        
dtypes: datetime64[ns](1), float64(3), int64(9), object(16)
memory usage: 15.7+ MB

Data cleaning finished!! 👏 Now! We are ready to answer questions and draw conclusions using our data. 👌 🍀

----Finish the data cleaning----

Time analysis

How many accidents were registered by the police in New York City in 2020 by date?

Number of accident per date (Jan-Aug 2020)

fig = px.line(dfg, x="crash_date", y="count"
              ,hover_data={"crash_date": "|%B %d, %Y"}
              ,markers=True
              ,color_discrete_sequence=px.colors.diverging.PRGn
             ,template = "plotly_white"
             )
fig.update_layout(
    title="Number of accidents by Date(Jan-Aug2020)"
    ,xaxis_title="date"
    ,yaxis_title="Number of accidents"

)

fig.update_xaxes(
    dtick="M1",
    tickformat="%b\n%Y",
    ticklabelmode="period")

fig.show()

Compare the % of total accidents by month.

fig3=px.bar(df_stack
            ,x='Percentage'
            ,y='year'
            ,color = 'month'
            , orientation='h'
           )

fig3.update_layout(title = "Percentage share of Accident by Month", 
     template = 'simple_white', xaxis_title = '%', 
     yaxis_title = 'year',
    legend_title_text='Month')

fig3

As we can observe, the number of accidents decreases since the end of February. One reason could be that fewer people are driving to work in these months during to COVID.

Break down accident frequency by day of week and hour of day. Based on this data, when do accidents occur most frequently?

As we did with months, we can analyze the distribution of car accidents according to the time and week by using a bar plot as well.

dfg = dfg.groupby(['hour','month_']).count().reset_index()
dfg.rename(columns = {"location": "count"}, inplace=True)
fig = px.bar(dfg, x="hour", y="count",color='month_')
fig.update_layout(
    title="Number of accidents by Time(Jan-Aug2020)"
    ,xaxis_title="Time"
    ,yaxis_title="Number of accidents"
    ,legend_title_text='Month'
)

fig.show()

As we can observe in the plot, the greater number of accidents occur in early-morning hours 14–18. Accidents tend to be more severe in the evening.

fig = px.bar(dfg, x="weekday", y="count",color='month_')
fig.update_layout(
    title="Number of accidents by Week(Jan-Aug2020)"
    ,xaxis_title="Time"
    ,yaxis_title="Number of accidents"

)

fig.show()

fig = px.bar(dfg, x="weekday", y="count")
fig.update_layout(
    title="Average Number of accidents by Week(Jan-Aug2020)"
    ,xaxis_title="Time"
    ,yaxis_title="Number of accidents")

fig.show()

As shown in the plot above, the number of car accidents decrease at the weekend. Weekdays present around an average of 280-300 car accidents per day, around 40 more accidents than on weekends.

Type of accident analysis

The data we are analyzing contains information related to (1) victims,(2) Contribution Factor, (3)vehicles. Regarding the type of accident the data frame includes information such as the number of injuries and killed,the contribution factor of the accident and the vehicles type involved in the accident.

Number of injuries and killed

The data frame includes information about how many victims were injuries and killed in each car accident. We can easily represent the percentage of injuries and kill using a pie plot as follows:

import plotly.express as px
df['number_of_injured']= df['number_of_persons_injured']
df.loc[df['number_of_persons_injured'] > 2, 'number_of_injured'] = '3-5'
df.loc[df['number_of_persons_injured'] > 5, 'number_of_injured'] = 'More than 5'

fig = px.pie(df, values='accident_count', names='number_of_injured', title='Injuries in 2020',color_discrete_sequence=px.colors.diverging.PRGn)
fig.show()

The plot shows that 73% of the accident did not have victims injured and 21% of accident have one victims injured.

import plotly.express as px
df['number_of_killed']= df['number_of_persons_killed']
df.loc[df['number_of_persons_killed'] > 2, 'number_of_killed'] = '3-5'
df.loc[df['number_of_persons_killed'] > 5, 'number_of_killed'] = 'More than 5'

fig = px.pie(df, values='accident_count', names='number_of_killed', title='Killed in 2020',color_discrete_sequence=px.colors.diverging.PRGn)
fig.show()

Less than 1% of the accident have victims killed in the car accident.

On which particular street were the most accidents reported? What does that represent as a % of all reported accidents?

Top 10 street reported accidents (Jan - Aug 2020)

taba = pd.crosstab(df.on_street_name, df.month_, values=df.location, aggfunc='count').round(0)
taba = taba.sort_values(by='January',ascending=False)
tabb = pd.crosstab(df.on_street_name, df.month_, values=df.location, aggfunc='count',normalize='columns').round(4)*100

tab = (
   pd.concat([taba, tabb],axis = 1, keys = ['count', '%'])
   .swaplevel(axis = 1)
   .sort_index(axis = 1, ascending=[True, False])
   .rename_axis(['month_', 'on_street_name'], axis = 1)
)

tab.head(10)

month_	January		February		March		April		May		June		July		August
on_street_name	count	%	count	%	count	%	count	%	count	%	count	%	count	%	count	%
on_street_name
BELT PARKWAY	166.0	1.71	209.0	2.25	170.0	2.29	68.0	2.48	103.0	2.54	120.0	2.39	145.0	2.44	137.0	2.42
BROOKLYN QUEENS EXPRESSWAY	141.0	1.45	129.0	1.39	115.0	1.55	40.0	1.46	51.0	1.26	65.0	1.29	65.0	1.09	88.0	1.55
GRAND CENTRAL PKWY	128.0	1.32	114.0	1.23	87.0	1.17	29.0	1.06	38.0	0.94	49.0	0.97	71.0	1.19	48.0	0.85
FDR DRIVE	125.0	1.29	108.0	1.16	129.0	1.74	77.0	2.81	65.0	1.60	48.0	0.95	62.0	1.04	72.0	1.27
LONG ISLAND EXPRESSWAY	125.0	1.29	137.0	1.47	106.0	1.43	42.0	1.53	46.0	1.13	76.0	1.51	93.0	1.56	78.0	1.38
MAJOR DEEGAN EXPRESSWAY	111.0	1.14	111.0	1.19	79.0	1.07	38.0	1.39	47.0	1.16	51.0	1.01	44.0	0.74	73.0	1.29
BROADWAY	101.0	1.04	103.0	1.11	80.0	1.08	27.0	0.99	32.0	0.79	70.0	1.39	73.0	1.23	73.0	1.29
ATLANTIC AVENUE	95.0	0.98	101.0	1.09	84.0	1.13	31.0	1.13	31.0	0.76	55.0	1.09	61.0	1.02	54.0	0.95
CROSS BRONX EXPY	89.0	0.92	83.0	0.89	85.0	1.15	24.0	0.88	54.0	1.33	48.0	0.95	51.0	0.86	72.0	1.27
3 AVENUE	86.0	0.89	81.0	0.87	80.0	1.08	16.0	0.58	27.0	0.66	48.0	0.95	43.0	0.72	37.0	0.65

What was the most common contributing factor for the accidents reported in this sample (based on Vehicle 1)? What about for fatal accidents specifically?

Top 10 contribution factor for accidents (Jan - Aug 2020)

taba = pd.crosstab(df.contributing_factor_vehicle_1, df.month_, values=df.location, aggfunc='count').round(0)
taba = taba.sort_values(by='January',ascending=False)
tabb = pd.crosstab(df.contributing_factor_vehicle_1, df.month_, values=df.location, aggfunc='count',normalize='columns').round(4)*100

tab = (
   pd.concat([taba, tabb],axis = 1, keys = ['count', '%'])
   .swaplevel(axis = 1)
   .sort_index(axis = 1, ascending=[True, False])
   .rename_axis(['month_', 'contributing_factor_vehicle_1'], axis = 1)
)

tab.head(10)

month_	January		February		March		April		May		June		July		August
contributing_factor_vehicle_1	count	%	count	%	count	%	count	%	count	%	count	%	count	%	count	%
contributing_factor_vehicle_1
Driver Inattention/Distraction	3398.0	25.93	3387.0	27.10	2803.0	27.54	919.0	24.36	1367.0	24.19	1738.0	24.91	2152.0	25.48	2042.0	25.46
Unspecified	3335.0	25.45	3074.0	24.60	2523.0	24.79	1021.0	27.06	1530.0	27.07	1929.0	27.65	2414.0	28.58	2323.0	28.97
Failure to Yield Right-of-Way	1002.0	7.65	944.0	7.55	663.0	6.51	203.0	5.38	314.0	5.56	420.0	6.02	509.0	6.03	477.0	5.95
Following Too Closely	961.0	7.33	938.0	7.51	754.0	7.41	150.0	3.98	310.0	5.49	384.0	5.50	463.0	5.48	420.0	5.24
Backing Unsafely	588.0	4.49	559.0	4.47	456.0	4.48	153.0	4.06	200.0	3.54	234.0	3.35	265.0	3.14	272.0	3.39
Passing or Lane Usage Improper	530.0	4.04	504.0	4.03	379.0	3.72	124.0	3.29	188.0	3.33	265.0	3.80	321.0	3.80	288.0	3.59
Passing Too Closely	528.0	4.03	497.0	3.98	390.0	3.83	113.0	2.99	194.0	3.43	235.0	3.37	294.0	3.48	307.0	3.83
Other Vehicular	378.0	2.88	364.0	2.91	242.0	2.38	109.0	2.89	182.0	3.22	230.0	3.30	275.0	3.26	250.0	3.12
Unsafe Lane Changing	338.0	2.58	348.0	2.78	260.0	2.55	57.0	1.51	107.0	1.89	126.0	1.81	151.0	1.79	149.0	1.86
Turning Improperly	247.0	1.88	251.0	2.01	219.0	2.15	59.0	1.56	100.0	1.77	117.0	1.68	162.0	1.92	148.0	1.85

Top 10 vehicle involve in accidents (Jan - Aug 2020)

taba = pd.crosstab(df.vehicle_type_code_1, df.month_, values=df.location, aggfunc='count').round(0)
taba = taba.sort_values(by='January',ascending=False)
tabb = pd.crosstab(df.vehicle_type_code_1, df.month_, values=df.location, aggfunc='count',normalize='columns').round(4)*100

tab = (
   pd.concat([taba, tabb],axis = 1, keys = ['count', '%'])
   .swaplevel(axis = 1)
   .sort_index(axis = 1, ascending=[True, False])
   .rename_axis(['month_', 'vehicle_type_code_1'], axis = 1)
)

tab.head(10)

month_	January		February		March		April		May		June		July		August
vehicle_type_code_1	count	%	count	%	count	%	count	%	count	%	count	%	count	%	count	%
vehicle_type_code_1
Sedan	5678.0	43.55	5482.0	44.08	4537.0	44.83	1887.0	50.37	2725.0	48.50	3295.0	47.47	4028.0	47.97	3876.0	48.60
Station Wagon/Sport Utility Vehicle	5265.0	40.38	4794.0	38.55	3877.0	38.31	1324.0	35.34	2022.0	35.99	2459.0	35.43	2858.0	34.04	2817.0	35.32
Taxi	596.0	4.57	672.0	5.40	455.0	4.50	86.0	2.30	133.0	2.37	172.0	2.48	239.0	2.85	224.0	2.81
Pick-up Truck	375.0	2.88	348.0	2.80	299.0	2.95	94.0	2.51	102.0	1.82	137.0	1.97	184.0	2.19	181.0	2.27
Box Truck	249.0	1.91	250.0	2.01	199.0	1.97	63.0	1.68	118.0	2.10	132.0	1.90	153.0	1.82	135.0	1.69
Bus	249.0	1.91	225.0	1.81	152.0	1.50	25.0	0.67	48.0	0.85	49.0	0.71	74.0	0.88	73.0	0.92
Tractor Truck Diesel	87.0	0.67	104.0	0.84	71.0	0.70	27.0	0.72	46.0	0.82	56.0	0.81	59.0	0.70	61.0	0.76
Van	58.0	0.44	70.0	0.56	55.0	0.54	20.0	0.53	35.0	0.62	54.0	0.78	59.0	0.70	39.0	0.49
Bike	57.0	0.44	57.0	0.46	75.0	0.74	37.0	0.99	94.0	1.67	145.0	2.09	163.0	1.94	156.0	1.96
Ambulance	43.0	0.33	49.0	0.39	41.0	0.41	24.0	0.64	36.0	0.64	27.0	0.39	53.0	0.63	34.0	0.43

Type of accident analysis — conclusions

(1) In most accidents, Sedan,Station Wagon/Sport Utility, or Taxi vehicles were involved. Nearly half of the accident involve Sedan

(2) Most of the accident did not have victims injured in car accidents in 2020 (73%).

(3) Accidents tend to be more severe during night, late-evening, and weekends.

(4) Driver Inattention/Distraction is the main reason for the car accident (around 25%).

Location analysis

The density map of the car accident in NYC (Jan-Aug 2020)

The best way to analyze spacial data is by using maps. Folium is a python library that helps you create several types of Leaflet maps. We can easily generate a map of New York City, creating a Folium Map object. The location argument allows to center the map in a specific location (in our case New York City). We can also provide an initial zoom level into that location to zoom the map into the center.

import folium

df['store_coords'] = None
df['lon'] = None
df['lan'] = None

# we will use regex to parse out liquor store coordinates
import re
for i, row in df.iterrows():
    store_loc_str = row['location']

    store_coord_str = re.search(r'\((.*?)\)',store_loc_str)
    if store_coord_str is None:
        continue

    store_coord_str = store_coord_str.group(1)
    store_coords = store_coord_str.split(' ')
    store_coords = [round(float(numeric_string),4) for numeric_string in store_coords]
    store_coords = store_coords[::-1]
    
    df['lon'] = store_coords[0]
    
    if len(store_coords) == 2:
        df.at[i, 'store_coords'] = store_coords
        df['lan'] = store_coords[1]

store_coords_lst = df['store_coords'].tolist()
store_coords_lst = [i for i in store_coords_lst if i]

import pandas as pd
import numpy as np

import seaborn as sns
from matplotlib import pyplot as plt
%matplotlib inline

import folium
from folium import plugins

liquor_map = folium.Map(
  location=[df['latitude'].mean(), df['longitude'].mean()],
  zoom_start = 15,
  prefer_canvas = True,
  disable_3d = True
)

liquor_map.add_child(
  plugins.HeatMap(
    data = store_coords_lst,
    radius = 15
  )
)

Make this Notebook Trusted to load map: File -> Trust Notebook

from folium.plugins import HeatMapWithTime

df_map = folium.Map(location=[df['latitude'].mean(), df['longitude'].mean()], zoom_start=12)

hour_list = [[] for _ in range(8)]
for lat,log,month in zip(df.latitude,df.longitude,df.month):
    hour_list[month-1].append([lat,log]) 

index = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug']
HeatMapWithTime(hour_list, index,auto_play=True).add_to(df_map)

df_map

Make this Notebook Trusted to load map: File -> Trust Notebook

!jupyter nbconvert --to html NYC_accidents_v3.ipynb

[NbConvertApp] Converting notebook NYC_accidents_v3.ipynb to html
[NbConvertApp] Writing 9686701 bytes to NYC_accidents_v3.html

Looking at the above timeline, we can observe how the number of accidents per month

Thanks for reading!!! 😊 🍀